Event-based dataset for the detection and classification of manufacturing assembly tasks
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916257691860992 |
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| author | Duarte, Laura Neto, Pedro |
| author_facet | Duarte, Laura Neto, Pedro |
| contents | The featured dataset, the Event-based Dataset of Assembly Tasks (EDAT24), showcases a selection of manufacturing primitive tasks (idle, pick, place, and screw), which are basic actions performed by human operators in any manufacturing assembly. The data were captured using a DAVIS240C event camera, an asynchronous vision sensor that registers events when changes in light intensity value occur. Events are a lightweight data format for conveying visual information and are well-suited for real-time detection and analysis of human motion. Each manufacturing primitive has 100 recorded samples of DAVIS240C data, including events and greyscale frames, for a total of 400 samples. In the dataset, the user interacts with objects from the open-source CT-Benchmark in front of the static DAVIS event camera. All data are made available in raw form (.aedat) and in pre-processed form (.npy). Custom-built Python code is made available together with the dataset to aid researchers to add new manufacturing primitives or extend the dataset with more samples. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_14626 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Event-based dataset for the detection and classification of manufacturing assembly tasks Duarte, Laura Neto, Pedro Computer Vision and Pattern Recognition The featured dataset, the Event-based Dataset of Assembly Tasks (EDAT24), showcases a selection of manufacturing primitive tasks (idle, pick, place, and screw), which are basic actions performed by human operators in any manufacturing assembly. The data were captured using a DAVIS240C event camera, an asynchronous vision sensor that registers events when changes in light intensity value occur. Events are a lightweight data format for conveying visual information and are well-suited for real-time detection and analysis of human motion. Each manufacturing primitive has 100 recorded samples of DAVIS240C data, including events and greyscale frames, for a total of 400 samples. In the dataset, the user interacts with objects from the open-source CT-Benchmark in front of the static DAVIS event camera. All data are made available in raw form (.aedat) and in pre-processed form (.npy). Custom-built Python code is made available together with the dataset to aid researchers to add new manufacturing primitives or extend the dataset with more samples. |
| title | Event-based dataset for the detection and classification of manufacturing assembly tasks |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2405.14626 |